{"doi":"10.1016/j.csbj.2023.11.008","title":"Preoperative <sup>18</sup> F-FDG PET/CT and CT radiomics for identifying aggressive histopathological subtypes in early stage lung adenocarcinoma","abstract":"Lung adenocarcinoma (ADC) is the most common non-small cell lung cancer. Surgical resection is the primary treatment for early-stage lung ADC while lung-sparing surgery is an alternative for non-aggressive cases. Identifying histopathologic subtypes before surgery helps determine the optimal surgical approach. Predominantly solid or micropapillary (MIP) subtypes are aggressive and associated with a higher likelihood of recurrence and metastasis and lower survival rates. This study aims to non-invasively identify these aggressive subtypes using preoperative 18F-FDG PET/CT and diagnostic CT radiomics analysis. We retrospectively studied 119 patients with stage I lung ADC and tumors ≤ 2 cm, where 23 had aggressive subtypes (18 solid and 5 MIPs). Out of 214 radiomic features from the PET/CT and CT scans and 14 clinical parameters, 78 significant features (3 CT and 75 PET features) were identified through univariate analysis and hierarchical clustering with minimized feature collinearity. A combination of Support Vector Machine classifier and Least Absolute Shrinkage and Selection Operator built predictive models. Ten iterations of 10-fold cross-validation (10×10-fold CV) evaluated the model. A pair of texture feature (PET GLCM Correlation) and shape feature (CT Sphericity) emerged as the best predictor. The radiomics model significantly outperformed the conventional predictor SUVmax (accuracy: 83.5% vs. 74.7%, p=9e-9) and identified aggressive subtypes by evaluating FDG uptake asymmetry in the tumor. It also demonstrated a high negative predictive value of 95.6% compared to SUVmax (88.2%, p=2e-10). The proposed radiomics approach could reduce unnecessary extensive surgeries for non-aggressive subtype patients, improving surgical decision-making for early-stage lung ADC patients.","journal":"Computational and Structural Biotechnology Journal","year":2023,"id":341845,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":15,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9559,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1077906,"name":"Chia‐Ju Liu","orcid":"0000-0003-1641-5617","position":1,"is_corresponding":false},{"id":379111,"name":"Sadegh Alam","orcid":"0000-0001-7845-4959","position":2,"is_corresponding":false},{"id":307645,"name":"Jung Hun Oh","orcid":"0000-0001-8791-2755","position":3,"is_corresponding":false},{"id":293293,"name":"Raj G. Vaghjiani","orcid":null,"position":4,"is_corresponding":false},{"id":255807,"name":"John L. Humm","orcid":"0000-0003-4245-5591","position":5,"is_corresponding":false},{"id":447113,"name":"Wolfgang Weber","orcid":"0000-0002-7854-4345","position":6,"is_corresponding":false},{"id":227919,"name":"Prasad S. Adusumilli","orcid":"0000-0002-1699-2046","position":7,"is_corresponding":false},{"id":87007,"name":"Joseph O. Deasy","orcid":"0000-0002-9437-266X","position":8,"is_corresponding":false},{"id":377111,"name":"Wei Lü","orcid":"0000-0003-0829-5440","position":9,"is_corresponding":false},{"id":379110,"name":"Wookjin Choi","orcid":"0000-0001-8038-5876","position":0,"is_corresponding":true}],"reference_count":73,"raw_metadata":null,"created_at":"2026-07-19T01:11:08.077724Z","pmid":"38034400","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}